| Status | Subtype | Assigned | Task | ||
|---|---|---|---|---|---|
| Resolved | • Ladsgroup | T151611 Enable ORES Review Tool on Czech Wikipedia | |||
| Resolved | • Ladsgroup | T156492 Train and test damaging/goodfaith models for Czech Wikipedia |
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# Model tuning report - Revscoring version: 1.3.5 - Features: editquality.feature_lists.cswiki.damaging - Date: 2017-01-28T17:42:35.310686 - Observations: 4925 - Labels: [false, true] - Scoring: roc_auc - Folds: 5 # Top scoring configurations | model | mean(scores) | std(scores) | params | |:---------------------------|---------------:|--------------:|:-------------------------------------------------------------------------------| | RandomForestClassifier | 0.834 | 0.016 | max_features="log2", criterion="entropy", n_estimators=640, min_samples_leaf=3 | | RandomForestClassifier | 0.834 | 0.017 | max_features="log2", criterion="entropy", n_estimators=160, min_samples_leaf=5 | | RandomForestClassifier | 0.833 | 0.016 | max_features="log2", criterion="entropy", n_estimators=640, min_samples_leaf=5 | | GradientBoostingClassifier | 0.831 | 0.021 | max_features="log2", learning_rate=0.01, n_estimators=500, max_depth=7 | | RandomForestClassifier | 0.831 | 0.017 | max_features="log2", criterion="entropy", n_estimators=320, min_samples_leaf=3 | | GradientBoostingClassifier | 0.831 | 0.021 | max_features="log2", learning_rate=0.01, n_estimators=700, max_depth=7 | | RandomForestClassifier | 0.83 | 0.02 | max_features="log2", criterion="entropy", n_estimators=640, min_samples_leaf=1 | | RandomForestClassifier | 0.83 | 0.012 | max_features="log2", criterion="entropy", n_estimators=160, min_samples_leaf=3 | | RandomForestClassifier | 0.83 | 0.015 | max_features="log2", criterion="entropy", n_estimators=640, min_samples_leaf=7 | | GradientBoostingClassifier | 0.83 | 0.019 | max_features="log2", learning_rate=0.01, n_estimators=500, max_depth=5 |
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Model for damaging:
- type: GradientBoosting - params: balanced_sample_weight=true, init=null, min_samples_split=2, center=true, subsample=1.0, min_weight_fraction_leaf=0.0, balanced_sample=false, n_estimators=500, random_state=null, presort="auto", warm_start=false, scale=true, verbose=0, max_depth=7, learning_rate=0.01, min_samples_leaf=1, max_features="log2", loss="deviance", max_leaf_nodes=null - version: 0.3.0 - trained: 2017-01-28T18:08:59.959144 Table: ~False ~True ----- -------- ------- False 4032 444 True 208 241 Accuracy: 0.868 Precision: ----- ----- False 0.951 True 0.348 ----- ----- Recall: ----- ----- False 0.901 True 0.534 ----- ----- PR-AUC: ----- ----- False 0.976 True 0.423 ----- ----- ROC-AUC: ----- ----- False 0.835 True 0.835 ----- ----- Recall @ 0.1 false-positive rate: label threshold recall fpr ------- ----------- -------- ----- False 0.856 0.534 0.088 True 0.508 0.547 0.091 Recall @ 0.98 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.837 0.56 0.982 True 0.885 0.059 1 Recall @ 0.9 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.118 0.997 0.914 True 0.881 0.067 0.991 Recall @ 0.45 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.095 1 0.91 True 0.64 0.409 0.474 Recall @ 0.15 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.095 1 0.91 True 0.144 0.903 0.161
Goodfaith:
- type: GradientBoosting - params: presort="auto", min_samples_leaf=1, max_leaf_nodes=null, min_samples_split=2, center=true, max_depth=5, balanced_sample_weight=true, min_weight_fraction_leaf=0.0, verbose=0, balanced_sample=false, random_state=null, max_features="log2", subsample=1.0, n_estimators=500, scale=true, warm_start=false, loss="deviance", learning_rate=0.01, init=null - version: 0.3.0 - trained: 2017-01-28T18:10:19.008889 Table: ~False ~True ----- -------- ------- False 159 59 True 522 4185 Accuracy: 0.882 Precision: ----- ----- False 0.23 True 0.986 ----- ----- Recall: ----- ----- False 0.722 True 0.889 ----- ----- PR-AUC: ----- ----- False 0.459 True 0.991 ----- ----- ROC-AUC: ----- ----- False 0.888 True 0.888 ----- ----- Recall @ 0.1 false-positive rate: label threshold recall fpr ------- ----------- -------- ----- False 0.568 0.708 0.087 True 0.847 0.576 0.075 Recall @ 0.98 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.914 0.111 1 True 0.337 0.951 0.982 Recall @ 0.9 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.914 0.111 1 True 0.079 1 0.957 Recall @ 0.45 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.773 0.465 0.518 True 0.079 1 0.957 Recall @ 0.15 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.267 0.836 0.17 True 0.079 1 0.957
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ScikitLearnClassifier - type: GradientBoosting - params: warm_start=false, min_samples_split=2, loss="deviance", init=null, min_weight_fraction_leaf=0.0, min_samples_leaf=1, max_depth=7, presort="auto", max_leaf_nodes=null, verbose=0, balanced_sample_weight=true, random_state=null, balanced_sample=false, max_features="log2", n_estimators=500, scale=true, center=true, learning_rate=0.01, subsample=1.0 - version: 0.3.0 - trained: 2017-01-28T20:18:51.260027 Table: ~False ~True ----- -------- ------- False 17812 1193 True 96 741 Accuracy: 0.935 Precision: ----- ----- False 0.995 True 0.383 ----- ----- Recall: ----- ----- False 0.937 True 0.884 ----- ----- PR-AUC: ----- ----- False 0.995 True 0.802 ----- ----- ROC-AUC: ----- ----- False 0.969 True 0.965 ----- ----- Recall @ 0.1 false-positive rate: label threshold recall fpr ------- ----------- -------- ----- False 0.572 0.916 0.093 True 0.369 0.918 0.094 Recall @ 0.98 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.122 0.997 0.981 True 0.919 0.345 0.996 Recall @ 0.9 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.051 1 0.963 True 0.88 0.562 0.912 Recall @ 0.45 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.051 1 0.963 True 0.61 0.863 0.474 Recall @ 0.15 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.051 1 0.963 True 0.16 0.955 0.175
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ScikitLearnClassifier - type: GradientBoosting - params: center=true, scale=true, min_weight_fraction_leaf=0.0, n_estimators=500, presort="auto", max_leaf_nodes=null, warm_start=false, verbose=0, max_features="log2", balanced_sample=false, init=null, loss="deviance", balanced_sample_weight=true, learning_rate=0.01, min_samples_leaf=1, min_samples_split=2, subsample=1.0, random_state=null, max_depth=5 - version: 0.3.0 - trained: 2017-01-28T22:26:31.693530 Table: ~False ~True ----- -------- ------- False 368 31 True 1079 18364 Accuracy: 0.944 Precision: ----- ----- False 0.254 True 0.998 ----- ----- Recall: ----- ----- False 0.922 True 0.945 ----- ----- PR-AUC: ----- ----- False 0.692 True 0.995 ----- ----- ROC-AUC: ----- ----- False 0.969 True 0.971 ----- ----- Recall @ 0.1 false-positive rate: label threshold recall fpr ------- ----------- -------- ----- False 0.31 0.951 0.081 True 0.368 0.954 0.088 Recall @ 0.98 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.952 0.21 1 True 0.046 1 0.983 Recall @ 0.9 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.94 0.334 0.936 True 0.046 1 0.983 Recall @ 0.45 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.839 0.779 0.476 True 0.046 1 0.983 Recall @ 0.15 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.306 0.945 0.197 True 0.046 1 0.983
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@Halfak If this is done (according to the column) I think we should close it as resolved, shouldn't we?
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This is now deployed in WMFLabs. Next step is production and then enabling the ORES Review Tool. Stay tuned.
https://ores.wmflabs.org/v2/scores/cswiki/?models=damaging|goodfaith&model_info=trained|type